A Point-to-Distribution Joint Geometry and Color Metric for Point Cloud Quality Assessment

TL;DR

This paper introduces a novel point-to-distribution metric for assessing point cloud quality, effectively combining geometry and color information, and demonstrating superior performance over existing metrics.

Contribution

A new joint geometry and color quality metric based on Mahalanobis distance for point clouds, enhancing assessment accuracy for lossy compression.

Findings

Outperforms existing point cloud quality metrics

Effectively combines geometry and color assessments

Demonstrates robustness across various distortions

Abstract

Point clouds (PCs) are a powerful 3D visual representation paradigm for many emerging application domains, especially virtual and augmented reality, and autonomous vehicles. However, the large amount of PC data required for highly immersive and realistic experiences requires the availability of efficient, lossy PC coding solutions are critical. Recently, two MPEG PC coding standards have been developed to address the relevant application requirements and further developments are expected in the future. In this context, the assessment of PC quality, notably for decoded PCs, is critical and asks for the design of efficient objective PC quality metrics. In this paper, a novel point-to-distribution metric is proposed for PC quality assessment considering both the geometry and texture. This new quality metric exploits the scale-invariance property of the Mahalanobis distance to assess first the geometry and color point-to-distribution distortions, which are after fused to obtain a joint geometry and color quality metric. The proposed quality metric significantly outperforms the best PC quality assessment metrics in the literature.